公开(公告)号：US10049325B2

公开(公告)日：2018-08-14

申请号：US13561704

申请日：2012-07-30

申请人： Hideki Shimomura ,  Kenta Kawamoto ,  Kohtaro Sabe

发明人： Hideki Shimomura ,  Kenta Kawamoto ,  Kohtaro Sabe

IPC分类号： G06F15/18 ,  G06N7/00

摘要： Provided is an information processing apparatus including a learning part performing learning of a model of an environment in which an agent performs action, using an observed value observed in the agent when the agent capable of action performs action, an action determining part determining action to be performed by the agent, based on the model, and a user instruction output part outputting instruction information representing an instruction from a user according to the instruction from the user, wherein the action determining part determines the action performed by the agent according to the instruction information when there is an instruction from the user.

公开(公告)号：US09766604B2

公开(公告)日：2017-09-19

申请号：US14234658

申请日：2012-07-25

申请人： Kenta Kawamoto ,  Yoshiaki Iwai ,  Satoru Shimizu

发明人： Kenta Kawamoto ,  Yoshiaki Iwai ,  Satoru Shimizu

IPC分类号： G06F3/048 ,  G05B15/02 ,  G05D1/00 ,  G06F3/0484

CPC分类号： G05B15/02 ,  G05B2219/40099 ,  G05B2219/40123 ,  G05B2219/40409 ,  G05B2219/40411 ,  G05D1/0038 ,  G05D2201/0217 ,  G06F3/04842

摘要： A control system, method and computer program product cooperate to assist control for an autonomous robot. The system includes a communications interface that exchanges information with the autonomous robot (22). A user interface displays a scene of location in which the autonomous robot (22) is positioned, and also receives an indication of a user selection of a user selected area within the scene. The communications interface transmits an indication of said user selected area to the autonomous robot (22) for further processing of the area by said autonomous robot (22).

公开(公告)号：US09091564B2

公开(公告)日：2015-07-28

申请号：US13564205

申请日：2012-08-01

申请人： Kenta Kawamoto ,  Hirotaka Suzuki

发明人： Kenta Kawamoto ,  Hirotaka Suzuki

IPC分类号： G05D11/00 ,  G01D4/00 ,  G06Q50/06 ,  H02J3/00

CPC分类号： G01D4/00 ,  G06Q50/06 ,  H02J3/00 ,  H02J2003/003 ,  H02J2003/007 ,  Y02B90/245 ,  Y02E60/76 ,  Y04S10/54 ,  Y04S10/60 ,  Y04S20/38 ,  Y04S20/40 ,  Y04S40/22

摘要： There is provided an information processing apparatus including: an appliance power consumption estimating unit estimating power consumption of each of a plurality of appliances disposed inside a large region that is divided into a plurality of regions; an appliance presence probability estimating unit estimating appliance presence probabilities that are probabilities that the respective appliances are present in the respective regions; a responsible share deciding unit deciding responsible shares that are proportions for respective people when power consumption in each region is shared among people who may be present in the large region; and a power consumption allocating unit calculating an allocated amount of power consumption of each person based on the power consumption of each of the plurality of appliances, the appliance presence probabilities, and the responsible shares.

摘要翻译： 提供了一种信息处理设备，包括：设备功耗估计单元，估计设置在被划分为多个区域的大区域内的多个设备中的每一个的功耗; 设备存在概率估计单元估计各个设备存在于各个区域中的概率的设备存在概率; 负责任的股份决定单位决定在大区域内的人员之间共享每个地区的电力消耗时各个人的比例的负责人; 以及功耗分配单元，基于所述多个器具中的每一个的功耗，所述器具存在概率和所述负责人的共享来计算每个人的分配的功耗量。

公开(公告)号：US08725510B2

公开(公告)日：2014-05-13

申请号：US12829984

申请日：2010-07-02

申请人： Yukiko Yoshiike ,  Kenta Kawamoto ,  Kuniaki Noda ,  Kohtaro Sabe

发明人： Yukiko Yoshiike ,  Kenta Kawamoto ,  Kuniaki Noda ,  Kohtaro Sabe

IPC分类号： G10L15/14

CPC分类号： G06N99/005

摘要： An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.

摘要翻译： HMM（隐马尔可夫模型）学习装置包括：用于学习作为代理可以执行的动作的函数的状态转移概率的学习单元，基于代理已经执行的动作执行的HMM的学习以及作出的时间序列信息 观察信号; 以及存储单元，用于将所述学习单元的学习结果存储为包括状态转换概率表和观察概率表的内部模型数据; 学习单元计算用于HMM状态转换和HMM观察概率的估计计算的频率变量; 存储单元保持状态转移概率表中分别对应于状态转换概率和每个观察概率的频率变量; 并且所述学习单元使用由所述存储单元保持的频率变量来执行学习，并且基于所述频率变量来估计所述状态转换概率和观察概率。

公开(公告)号：US20110010176A1

公开(公告)日：2011-01-13

申请号：US12829984

申请日：2010-07-02

申请人： Yukiko YOSHIIKE ,  Kenta Kawamoto ,  Kuniaki Noda ,  Kohtaro Sabe

发明人： Yukiko YOSHIIKE ,  Kenta Kawamoto ,  Kuniaki Noda ,  Kohtaro Sabe

IPC分类号： G10L15/14

CPC分类号： G06N99/005

摘要： An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.

摘要翻译： HMM（隐马尔可夫模型）学习装置包括：用于学习作为代理可以执行的动作的函数的状态转移概率的学习单元，基于代理已经执行的动作执行的HMM的学习以及作出的时间序列信息 观察信号; 以及存储单元，用于将所述学习单元的学习结果存储为包括状态转换概率表和观察概率表的内部模型数据; 学习单元计算用于HMM状态转换和HMM观察概率的估计计算的频率变量; 存储单元保持状态转移概率表中分别对应于状态转换概率和每个观察概率的频率变量; 并且所述学习单元使用由所述存储单元保持的频率变量来执行学习，并且基于所述频率变量来估计所述状态转换概率和观察概率。

公开(公告)号：US20100318478A1

公开(公告)日：2010-12-16

申请号：US12791240

申请日：2010-06-01

申请人： Yukiko Yoshiike ,  Kenta Kawamoto ,  Kuniaki Noda ,  Kohtaro Sabe

发明人： Yukiko Yoshiike ,  Kenta Kawamoto ,  Kuniaki Noda ,  Kohtaro Sabe

IPC分类号： G06N5/02 ,  G06F15/18

CPC分类号： G06N3/006 ,  G06K9/00335 ,  G06K9/00664 ,  G06K9/6297

摘要： An information processing device includes: a calculating unit configured to calculate a current-state series candidate that is a state series for an agent capable of actions reaching the current state, based on a state transition probability model obtained by performing learning of the state transition probability model stipulated by a state transition probability that a state will be transitioned according to each of actions performed by an agent capable of actions, and an observation probability that a predetermined observation value will be observed from the state, using an action performed by the agent, and an observation value observed at the agent when the agent performs an action; and a determining unit configured to determine an action to be performed next by the agent using the current-state series candidate in accordance with a predetermined strategy.

摘要翻译： 一种信息处理设备，包括：计算单元，被配置为基于通过执行状态转移概率的学习获得的状态转移概率模型来计算作为达到当前状态的动作的代理的状态序列的当前状态序列候选 由状态转移概率规定的模式，其状态将根据由能够执行动作的代理执行的动作而转变，并且使用由代理执行的动作从该状态观察到预定观察值的观察概率， 以及当代理人执行动作时在代理处观察到的观察值; 以及确定单元，被配置为根据预定策略来确定所述代理使用所述当前状态序列候选来执行的动作。

公开(公告)号：US07840508B2

公开(公告)日：2010-11-23

申请号：US11732807

申请日：2007-04-04

申请人： Yukiko Yoshiike ,  Masato Ito ,  Katsuki Minamino ,  Hirotaka Suzuki ,  Kenta Kawamoto

发明人： Yukiko Yoshiike ,  Masato Ito ,  Katsuki Minamino ,  Hirotaka Suzuki ,  Kenta Kawamoto

IPC分类号： G06F15/18 ,  G06N3/08

CPC分类号： G06N3/02

摘要： An information processing apparatus includes a storage unit configured to store a node holding dynamics; an input-weight-coefficient adjuster configured to adjust input-weight coefficients on a dimension-by-dimension basis, the input-weight coefficients being weight coefficients for individual dimensions of input data input to input units of the node, the input data being observed time-series data having a plurality of dimensions; and an output-weight-coefficient adjuster configured to adjust output-weight coefficients on a dimension-by-dimension basis, the output-weight coefficients being weight coefficients for individual dimensions of output data having a plurality of dimensions and output from output units of the node.

摘要翻译： 一种信息处理装置，包括：存储单元，被配置为存储保存动态的节点; 输入权重系数调整器，其被配置为在逐个维度的基础上调整输入权重系数，输入权重系数是输入到节点的输入单元的输入数据的各个维度的权重系数，观察到的输入数据 具有多个维度的时间序列数据; 以及输出权重系数调整器，其被配置为在逐个维度的基础上调整输出权重系数，所述输出权重系数是具有多个维度的输出数据的各个维度的权重系数，并且从所述输出单元的输出单元输出 节点。

公开(公告)号：US07587069B2

公开(公告)日：2009-09-08

申请号：US12075080

申请日：2008-03-07

申请人： Javier R. Movellan ,  Marian S. Bartlett ,  Gwendolen C. Littlewort ,  John Hershey ,  Ian R. Fasel ,  Eric C. Carlson ,  Josh Susskind ,  Kohtaro Sabe ,  Kenta Kawamoto ,  Kenichi Hidai

发明人： Javier R. Movellan ,  Marian S. Bartlett ,  Gwendolen C. Littlewort ,  John Hershey ,  Ian R. Fasel ,  Eric C. Carlson ,  Josh Susskind ,  Kohtaro Sabe ,  Kenta Kawamoto ,  Kenichi Hidai

IPC分类号： G06K9/00

CPC分类号： G06K9/00248 ,  G06K9/00308 ,  G06K9/6256 ,  G06N3/004

摘要： A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired. In detection, using an abort threshold value that has been learned in advance, whether provided data can be obviously judged as a non-face is determined every time one weak hypothesis outputs the result of discrimination. If it can be judged so, processing is aborted. A predetermined Gabor filter is selected from the detected face image by an Adaboost technique, and a support vector for only a feature quantity extracted by the selected filter is learned, thus performing expression recognition.

公开(公告)号：US07369686B2

公开(公告)日：2008-05-06

申请号：US10399740

申请日：2002-08-21

申请人： Jun Yokono ,  Kohtaro Sabe ,  Kenta Kawamoto

发明人： Jun Yokono ,  Kohtaro Sabe ,  Kenta Kawamoto

IPC分类号： G06K9/00

CPC分类号： G06K9/00288 ,  G06K9/00221 ,  G06K9/00228

摘要： A robot includes a face extracting section for extracting features of a face included in an image captured by a CCD camera, and a face recognition section for recognizing the face based on a result of face extraction by the face extracting section. The face extracting section is implemented by Gabor filters that filter images using a plurality of filters that have orientation selectivity and that are associated with different frequency components. The face recognition section is implemented by a support vector machine that maps the result of face recognition to a non-linear space and that obtains a hyperplane that separates in that space to discriminate a face from a non-face. The robot is allowed to recognize a face of a user within a predetermined time under a dynamically changing environment.

摘要翻译： 机器人包括：面部提取部，用于提取由CCD照相机拍摄的图像中包含的面部的特征;以及面部识别部，其基于面部提取部的面部提取的结果识别脸部。 人脸提取部分由Gabor滤波器实现，该滤波器使用具有取向选择性并且与不同频率分量相关联的多个滤波器对图像进行滤波。 脸部识别部分由支持向量机实现，该支持向量机将人脸识别的结果映射到非线性空间，并获得在该空间中分离的超平面，以将脸部与非脸部区分开。 允许机器人在动态变化的环境下在预定时间内识别用户的脸部。

公开(公告)号：US20070257910A1

公开(公告)日：2007-11-08

申请号：US10593150

申请日：2005-03-17

申请人： Steffen Gutmann ,  Masaki Fukuchi ,  Kotaro Sabe ,  Kenta Kawamoto

发明人： Steffen Gutmann ,  Masaki Fukuchi ,  Kotaro Sabe ,  Kenta Kawamoto

IPC分类号： G06T17/00

CPC分类号： G06K9/00664 ,  B62D57/024 ,  G06K9/00201 ,  G06T7/13 ,  G06T7/73 ,  G06T2207/30252

摘要： In a plane detection apparatus, a plane detection unit (3) includes a line fitting block (4) to select a group of distance data points being in one plane from distance data forming an image and extract lines from the distance data point group, and a region growing block (5) to detect one or more planar regions existing in the image from a group of all lines included in the image and extracted by the line fitting block (4). The line fitting block (4) first draws a line D1 connecting end points of the distance data point group, searches a point of interest brk whose distance to the line L1 is largest, segments the data point group by the point of interest brk when the distance is larger than a predetermined threshold, and determines a line L2 by the least-squares method when the distance is smaller than the predetermined threshold. In case there exists a larger number of data points than a predetermine number on one side of the line L2, the data point group is determined to be in a zig-zag shape, the data point group is segmented by the point of interest brk. These operations are done repeatedly. Thus, a plurality of planes robust against noises is detected simultaneously and accurately from distance data including measurement noises.

摘要翻译： 在平面检测装置中，平面检测装置（3）包括线拟合块（4），用于从形成图像的距离数据中选择位于一个平面中的一组距离数据点，并从距离数据点组提取线， 区域生长块（5），用于检测图像中存在的一个或多个平面区域，所述平面区域包括在所述图像中并由所述线拟合块（4）提取。 线拟合块（4）首先画出连接距离数据点组的端点的线D1，搜索距离线L 1最大的距离的兴趣点brk，将数据点组划分为兴趣点brk 当距离大于预定阈值时，并且当距离小于预定阈值时，通过最小二乘法确定线路L 2。 在线L2的一侧存在比预定数量多的数据点的情况下，数据点组被确定为Z字形，数据点组被兴趣点分割 。 这些操作重复完成。 因此，从包括测量噪声的距离数据同时且准确地检测到抵抗噪声的多个平面。